Inference on inspiral signals using LISA MLDC data

(Christian Roever, Ed Bloomer, James Clark, Nelson Christensen, 
Martin Hendry, Chris Messenger, Renate Meyer, Matt Pitkin, 
Alexander Stroeer, Jennifer Toher, Richard Umstaetter, 
John Veitch, Alberto Vecchio, Graham Woan)


Presented is a report on the progress in setting up an MCMC sampler to do
inference on binary inspiral signals in LISA data within a Bayesian
framework. We implemented a general scheme in which arbitrarily parameterised
signal waveforms may be injected, and where the likelihood then is computed
in the frequency domain. The initial results of MCMC runs on binary inspiral
(Mock LISA Data Challenge) signals of 2.0 post-Newtonian order, characterised
by 9 parameters, are given.